Three years ago, I joined Weibo Recommendation department. At the time, the recommendation system was at the very early stage, our services were evolving very quickly. One year later, advertising system has also been integrated with the recommendation system. Consequently, a new layer called Unified Value Evaluation (UVE) emerged. UVE became the unified endpoint for recommendation and advertising system.

The first 5 versions(yes, iterated very quickly) of UVE were implemented with Nginx/PHP-FPM. Unfortunately, UVE service depends on many internal resources such as Redis, RPC, etc. But PHP is not good at handling TCP connections in persistent way.

After some investigation and tests, we decided to choose OpenResty(Nginx/Lua) as the solution, for it can work with many existing PHP services seamlessly. And finally, the UVE Core Framework in Lua.


Main features compared to the previous PHP version:

  • high performance
  • less resource(CPU, RAM) usage
  • configurable
  • easy to deploy
  • unified log format


The architecture was very specialized for our requirements, so it’s not a general purpose web framework.

UVE Core Architecture

The “UVE Core” is the main procedure which strings together all other components:

  • The common components, such as Logger, Redis Utils, etc.
  • The business components, such as GlobalStrategy, Service Modules, etc.

The main procedure workflow:

UVE Core Workflow

Each stage of the workflow is modularized and can be configured as need.

The Service Modules and scheduling is the most important part of the workflow. Although the module scheduler can be implemented in different ways, the following diagram is our ad hoc implementation:

UVE Core Service Modules Scheduler

Log Format

A semi-structured log format was introduced with this framework. The purpose of the log format is easy to process with good extensibility.

The log format defines three level, but more sub-level can be implemented as needed.

UVE Core Log Format

The log is constructed from key:value basic fields, each level can have as many key:value fields as possible. Fields are separated with separator of different level, the separators of the three level are defined below.

Level 1 separator

Group Separator: 0x1A. Field Separator: 0x1C.

Level 2 separator

Group Separator: 0x01. Field Separator: 0x1D.

Level 3 separator

Group Separator: 0x1E. Field Separator: 0x1F.

Every level has a Group Separator and a Field Separator. In practice, the Level 1 only has one group, so the Group Separator is only used to normalize the log format which makes it easier to process the log recursively.

Following is an example log which has been parsed to JSON:

   "feedsnum": "2",
   "from": "1051513010",
   "platform": "iphone",
   "reqtime": "1427178973",
   "service_name": "article",
   "tmeta_l2": [
           "authoruid": "1633520291",
           "bgid": "1234567890123456",
           "category": "nr",
           "key": "article_app",
           "oid": "1022:1001603821084397949239",
           "tmeta_l3": [
                   "itemid": "1234567890",
                   "loc": "1",
                   "recotype": "arrarticle",
                   "ruletype": "a"
                   "itemid": "1234567891",
                   "loc": "2",
                   "recotype": "arrarticle",
                   "ruletype": "a"
           "authoruid": "1496913734",
           "bgid": "2234567890123456",
           "category": "ad",
           "key": "article_app",
           "oid": "1022:1001603821496853214821",
           "tmeta_l3": [
                   "itemid": "3334567890",
                   "loc": "3",
                   "recotype": "arrarticle",
                   "ruletype": "b"
                   "itemid": "6634567891",
                   "loc": "4",
                   "recotype": "arrarticle",
                   "ruletype": "b"
   "uid": "1716016202",
   "version": "5.1.5"


Stats Log Parser in Java

Service Endpoint

The default endpoint looks like this:

The SERVICE_NAME will be used as the service identifier to load corresponding configuration.

The URL pattern can be customized to meet different requirements.


Configuration is implemented as a Lua module. The out most attribute is the same as the the SERVICE_NAME described in preceding section. Here is an example:

module(..., package.seeall)

profile = {
   modules = {
       [25] = '',
       [26] = '',
   scheduler = 'uve.scheduler.profile',
   init = 'uve.init.profile',
   strategy = 'uve.strategy.profile',
   prerender = 'uve.prerender.profile',
   render = 'uve.render.localrender',
   postrender = 'uve.postrender.profile',
   postrequest = 'uve.postrequest.profile',

Notice the attributes of the configuration are corresponding to the workflow stages. Only the modules and render are required, others attributes are optional, depending on your requirements.


As described in preceding sections, the UVE Core Framework is dedicated for Weibo advertising and recommendation services, so it’s not expected to be a general purpose web framework. But it might be useful for guys who have similar requirements with us, or it can be an example of OpenResty.